Project Abstract When we consider buying a book on Amazon's Website, we often benefit from items listed in a section called Customers also viewed. These recommendations, generated by a method called collaborative filtering (CF), suggest items of possible interest based on what other customers have viewed and purchased. However, when clinicians search the electronic health record (EHR) with regard to a particular patient problem, the EHR does not make suggestions for potentially useful information. Instead, it requires clinicians to go through the same manual, cumbersome and laborious process of searching for and retrieving information for similar patients/problems every single time. This limitation is magnified in high-risk situations, such as managing chest pain in the emergency department (ED). The goal of this project is to implement and evaluate CF as a method to improve information retrieval from EHRs and reduce cognitive overload. The central hypothesis of our proposal is that CF will (1) help clinicians retrieve and review the right patient information more efficiently and effectively than current methods; and (2) score higher on usefulness and ease-of-use than current EHRs. We will implement our CF algorithms in CareWeb Plus, a SMART-on-FHIR app we are currently building to integrate relevant information from the Indiana Network for Patient Care (INPC), Indiana's major health information exchange, with the ED workflow in Cerner/Epic. Our aims are to (1) extend CareWeb Plus to support collaborative filtering; (2) design and implement collaborative filtering algorithms; (3) and implement and evaluate CareWeb Plus in two adult emergency departments. Over 190 clinicians will use and evaluate CareWeb Plus in the two busiest emergency departments in Indianapolis (> 200,000 patient visits/year collectively), with more than 13,000 of them related to chest pain. We will evaluate (1) process measures, such as CareWeb Plus use, information retrieval and viewing patterns, and time to key decisions (first order, admission, discharge); (2) outcomes variables, such as lab/procedure utilization, ED length of stay and admission rate; and (3) user perceptions and attitudes regarding usefulness and usability. Our project is significant because it addresses two current, major limitations of EHRs in clinical practice. (1) Clinicians have difficulty reviewing voluminous patient-specific information, especially from multiple sources, efficiently to find relevant facts, especially in time-sensitive situations. (2) EHR users have little to no ability to change the static and inflexible nature of EHR interfaces for information retrieval. Our proposal is innovative because it uses CF, a method for tailoring information retrieval well-established in many fields except healthcare, to help solve these two problems. Collaborative filtering will provide a continually adapting, dynamic paradigm of informational retrieval and presentation that naturally follows the evolution of clinical practice. In addition, our recommendations are generated from both physicians and patients, and thus go beyond the traditional scheme of CF algorithms that only look at user or item relations to generate recommendations.